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Applications of Computer Simulation Modeling for Health
Care Process Management
Michael CarterMechanical & Industrial Engineering Mechanical & Industrial Engineering
University of Toronto University of Toronto
IHD 2007 2
Overview of Presentation• Computer Simulation Modelling• CHEO ED – Ottawa• Hamilton Cardiac Surgery• System Dynamics: Toronto Western
and Ottawa Hospital ED• Some Challenges
IHD 2007 3
Computer Simulation• Many packages available• MedModel, Simul8 and Arena• Illustrate with MedModel• Patients arrive randomly • Arrival rate varies over the day• Wait for triage, MD, RN, labs, etc.• Add resources, increase rates, “What if?”
IHD 2007 4
• Location Statistics examples:• Crit exam1: 76.02% util.; • Crit exam1: avg. time: 59.71 min.• Avg. time in waiting room: 107.6 min
• MD: 73.5% util.; • RN: 27.55% util
• Also find the avg. time per patient in the ED: 113.11 min
Resource Statistics
IHD 2007 7
CHEO: Emergency Room
• Children’s Hospital of Eastern Ontario: Ottawa 1993
• Paediatric Teaching Hospital• 50,000 patient visits per year in the ER
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CHEO: Waiting Times (1993)
05
101520253035404550
0-1 hr 1-2 hr 2-3 hr 3-4 hr 4 plus
% pat's.
IHD 2007 9
CHEO: Emergency Room• 20 % of patients wait over two hours• Eleven suggestions by staff• Simulation used to evaluate scenarios• Fast track clinic• New Casualty Officer• Staggered start times
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Cardiac Surgery Simulation• Hamilton Health Sciences• Opening fourth cardiac OR in Spring
2006• How should OR time be allocated?• How many beds are required in
ICU/ward?• “What if?” Simulation tool
Surgery GroupingCardiac Surgery 2002-2004
N>4000
Redo/Combined
No Redo/Combined
CABGVALVECOTHRCONGD
CAVLVAORTA
CABG 1,2,3TVR,AVRCONGDCOTHR
CABG4,5,6,7MVR
CABGVALVEAORTA
CAVLVCOTHR
IHD 2007 12
Cardiac Surgery 2002-2004
Intermediate322 mins
n=281359
In-btwn284 mins
n=890313
Minor244 minsn=1016
266
Major 1353 mins
n=116
Major 2431 mins
n=60
IHD 2007 13
Surgery Duration Distribution
0
100
200
300
120
180
240
300
360
420
480
540
600
Surgery duration (mins)
0
100
200
300
120
180
240
300
360
420
480
540
600
660
Mor
e
Surgery duration (mins)
020406080
120
200
280
360
440
520
600
680
760
840
Surgery Duration (mins)
0102030
230
320
410
500
590
680
770
860
950
1040
1130
Surgery Duration (mins)
Minor246 minsn=1530
In-btwn285 minsn=1789
Intermediate337 mins
n=499
Major461 mins
n=220
IHD 2007 14
Performance Indicators– Number of cases completed/year– Cancellation rates
• Lack of ICU/ ward bed• Out of scheduled time• More urgent case took precedent
– Operating room utilization• Under-utilization (hours/week)• Overtime (hours/week)
– Ward bed utilization (ICU & CSU)
11 hour OR
0.0
0.5
1.0
1.5
2.0
1 major1 + 1 minor
1 intermediate + 1 in-between
1 intermediate + 1 minor
2 in-between
1 in-between + 1 minor
Combinations
Undertime & Overtime
(hour/day)
0
50
100
Total Cancellations (Cases/year)Undertime
OvertimeTotal Cancellations
Which procedures should be booked together?
IHD 2007 16
Planning ICU and Ward Capacity
0
5
10
15
20
25
30
35
unit/year
Mon Tue Wed Thu Fri Sat Sun
ICUcancel (#cancellations)CSUover (# daysexceeded 30 beds)
IHD 2007 17
General Internal Medicine Resource Allocation at Toronto Western Hospital:
A System Dynamics Approach
Hannah Wong
Healthcare Resource Modelling Lab,
Mechanical & Industrial Engineering, University of Toronto
The ED and GIM units at TWH are functioning over capacity, struggling daily to cope. Decisions that affect the whole hospital are made in silos.
TWH needs a way to explore the interactions of ED & GIM with each other, other departments within the hospital, and with the outside world.
A System Dynamics Approach
ObjectivesGain a better understanding of the influence GIM has on the ED.
Explore the impact of hospital policy on ED-GIM patient flow.
Redesign policies to better allocate resources.
GIM Patient Flow
“Valves” control the rate of inflow and outflow
“Stocks” are where patients accumulate
Patient flow
Source or sink (stocks outside model boundary)
ED Wait GIM/ED GIM ALC LTC
Death
Home
ED
ED Wait GIM/ED GIM ALC LTC
Death
Home
ED
LOS
9.1 hrs
LOS
26.2 days
LOS
15.7 days
LOS
7.5 days
Current Situation
Delay62 min
Delay27.9 hrs
Delay9.4 days
I’d be interested in knowing how much we’d need to decrease patient LOS to impact ED waiting times.
I’m interested in the bed question. E.g. If we add an additional 12 beds, what is the impact on flow?
How is LOS affected if:
5 additional (or any #) of patients are discharged on Friday
All patients are discharged before 3pm weekdays
ED patients move directly to floors and do not spend time in ED.
If we’re able to cut down the time it takes to:
Identify a patient as needing to go to inpatient Rehab/CCC (ALC facility)
Complete and send off applications
Match patients to available ALC beds quicker
What is the impact to GIM LOS, ALC days, etc.?
Questions for the Model to Consider
Using System Dynamics, we can model the effect of:
ED waiting room occupancy on rates of patients who leave without being seen
ED bed occupancy on ED waiting room LOS
GIM bed occupancy on GIM LOS in ED
ALC days on GIM occupancy
Hour of day and day of week trends in GIM discharge…how does this affect the ED?
Changes in the GIM patient population (diagnosis, discharge destination, etc)
IHD 2007 23
Objectives• Help hospital decision makers make more
informed decisions by providing insights into the system.
• Explore relationships between resource levels and bottlenecks in patient flow
• determine best allocation of scarce resources (nurses, beds, etc.) to meet objectives (eg., shorter LOS in ED)
w a it in gr o o m
T r ia g e
w a it in gr o o m 2
fa s t t r a c k
c o r r id o r
o b s e r v a t io n
e v a lu a t io n
r e s u s c ita t io n
p s y c h ia ty
h o m e
lw b s
g e n e r a lm e d ic in e
g e n e r a ls u r g e r y c a r d io lo g y o r th o p a e d ic s
h o m e c a r e
A L C
lo n g te r m c a r e
a r r iv a ls
a w a it E D b e d
to lw b s to ft
to c o r
to o b s
to e v a l
to r e s u s
to p s y c h
ft- hc o r - h
o b s - he v a l- h
r e s u s - hp s y - h
c o r - g m c o r - g s c o r - c a r c o r - o r t
o b s - g m o b s - g s o b s - c a r o b s - o r t
e v a l- g m e v a l- g s e v a l- c a r e v a l- o r t
r e s - g m r e s - g s r e s - c a r r e s - o r t
p s y - g m ,
g m - h g s - h c a r - h o r t- h
g m - h c g s - h c c a r - h c o r t- h c
g m - a lc g s - a lc c a r - a lc o r t- a lc
g m - ltc g s - ltc c a r - ltc o r t- ltc
to tr ia g e
The Ottawa Hospital ED Patient Flows
Rate adjusted by:-hour of day-season
Rates at which pts. get ED bed depends on:-Time of day-# of pts. in waiting room
Admission rate depends on admitting services’:
-Nurse to patient ratio
-nursing shift in
ED LoS depends on:-Area-If consult needed
ED bed type depends on-pt. severity (CTAS) which is dependent on age
Inpt. los depends on:-Service-Discharge destination
Model Validation
Model Accuracy: ED LOS
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
c-no c-yes e-no e-yes o-no o-yes P R F
% d
evia
tion
winter springsummer autumn
yes - with consultno - without consult
% deviation: (real los – model los)
real lose.g.:corridor, no consult c-no):
real: 5.3 hrsmodel: 4.6 hrs
Hence, model has 12% deviation from real
Note: Summer results (June, July, August, 2003) are noticeably different. SARS effect
The model can help answer the following questions:
How will wait times change in the ED if inpatients are directly admitted without a visit from a consult?
What will happen if we add/reduce # of GM beds? GS beds?
If adding a nurse, which service should one be added to in order to have greatest impact on ED los?
What will happen to ED LoS if there is a x% increase in patients 90 years old or older??
Scenario Testing
IHD 2007 27
Generalized Simulation of Ontario Emergency Departments:
The CROWDED study
MW Carter1, DJT Fernandes1,2, MJ Schull2, GS Zaric3, G Geiger4
1 Healthcare Productivity Laboratory, Mechanical & Industrial Engineering, U of Toronto; 2 Institute for Clinical Evaluative Sciences;
3 Richard Ivey School of Business, University of Western Ontario4Sunnybrook and Women’s College Health Sciences Centre
IHD 2007 28
Background• ED overcrowding and waiting - major problem• Most analysis based on ED-LOS data • A few simulation models - typically model ED-
LOS• Does not help us analyze improvements• Wanted to understand what happens in an
ED
IHD 2007 29
Potential Opportunities• Evaluation of alternatives (e.g., CTAS 4, 5)• Best practices• Identify bottlenecks• Impact on wait times of:
– faster consults– improved MRI process– additional nurses, doctors, treatment rooms,
etc.– reduction in average time to admit
IHD 2007 30
Opportunities (cont.)• Evaluate various policy scenarios:
– Alternate staff schedules
– Focused care-areas (e.g., for Cardiac).
– Impact of changing demographics (e.g., study role of non-emergent pts on LOS)
– Analysis of Fast Track clinic option
IHD 2007 31
• CROWDED study: Causes and Relationships of Overcrowding and Waiting in Different Emergency Depts.
• Two full time research assistants for one year• One full time PhD student: Dominic Fernandes• Cross section of hospitals in type & geography
– 3 rural, 4 community and 3 teaching from each geographic area of Ontario
IHD 2007 32
The Hospital Partners• Academic
– Kingston General– Sunnybrook &
Women's– London HSC
• Rural– Quinte Health Corp– Stevenson
Memorial– South Muskoka
• Community– Royal Victoria -
Barrie– Sudbury Regional– Markham-
Stouffville– Windsor Regional
IHD 2007 33
Generalized Model• Build ten individual ED models• Use components to build a general model• Design an interface to allow user to create
their own model
IHD 2007 34
Data Collection• Three visits to each hospital:
– pre-visit: three days– two data collection visits: seven days each
• Followed individual patients and tracked around 60 different processes– included triage, nurse- and physician-assessments,
lab and imaging activities, consults and disposition– Patient data include presenting problem, diagnosis,
mode of arrival, age, sex, and disposition
IHD 2007 35
Data Analysis
• Approx. 2200 patients tracked • Data is being used:
– for process time distributions– to determine patient pathways
• Approximately 25 patient-types identified ~90% of pts.
• Pareto Rule: 80% of patients fall under 20% of possible diagnoses
MD-Tracking• MD-activity characteristics will be used to define resource availability in simulation model.
• Different trends can be observed among hospital types.
Pt. Ass't
Documentation
Disc'n w/ Nurse
Review Chart
Follow-Up Ass't
Phone Consult
Pt. Treatm
ent
Review Image/Rpt
General Teaching
Break
Other RuralCommunity
Academic
0
5
10
15
20
25
30
35
Perc
enta
ge o
f Tim
e
Physician Activity Hospital Type
IHD 2007 37
Challenges• Doctors are hard to track (multi-tasking)• Missing data (long LOS; trauma)• Layout issues • Off-site fast-track clinic • Wait time before triage• Unplanned critical events (SARS, Norwalk)• Preemption• Administrative issues (suspicion, turnover)
IHD 2007 38
Conclusions• Simulation is a powerful tool for analyzing
emergency departments• It is not as simple as it looks: Most of my
projects took over a year– I wrote a book chapter on Challenges
• It is worth the time and effort
IHD 2007 39
Readings• “Operations Research and Health
Care: A Handbook of Methods and Applications” International Series in Operations Research and Management Science, Vol. 70, Brandeau, Sainfort & Pierskalla (Eds.) 2004, 872 p.Chapter 8: Carter & Blake “Using
Simulation in an Acute Care Hospital: Easier Said Than Done”
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